Financial Supply Chain

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The Democratisation of Data: Bringing Working Capital Full Circle At a time of unprecedented uncertainty, cross-functional data dashboards and machine learning could help treasurers to break down barriers and unlock hidden working capital efficiencies across their organisation.

The Democratisation of Data: Bringing Working Capital Full Circle

The Democratisation of Data: Bringing Working Capital Full Circle

By Bruce Meuli, Global Transaction Services Advisory Executive, EMEA, Bank of America


Working capital is often viewed by treasurers through a cash and liquidity lens. But this laser focus means that opportunities for efficiency can be slipping through the cracks. At a time of unprecedented uncertainty, cross-functional data dashboards and machine learning could help treasurers to break down barriers and unlock hidden working capital efficiencies across their organisation.

 

Bruce Meuli

Bruce Meuli
Global Transaction Services Advisory Executive, EMEA,
Bank of America

When members of the British Cycling Federation began taking their own pillows away with them on training camps, their performance improved. So much so that the team won eight gold medals at the London 2012 Olympics. This success was not all down to their sleeping paraphernalia, of course. It was part of their trainer’s new ‘marginal gains’ approach – the idea that if you break down everything that goes into riding a bike, and then improve each part by 1%, the cumulative result is a significant increase in performance.

This notion of looking at an entire process and identifying areas for continuous improvement translates equally well into the world of working capital. Too often, treasurers view working capital only through their own eyes – concentrating on cash and liquidity, while finance directors typically translate ‘working capital’ as ‘the financial supply chain’. This is understandable given the departmental siloes that still exist in most organisations, but this compartmentalised approach means many corporates do not have a firm handle on their end-to-end working capital management.


Drivers of change

Now more than ever, companies require optimisation across their complete working capital lifecycle – from capital-raising activities to the opening balance of cash, closing balance of cash, and the generation of cash throughout the business. During times of uncertainty and volatility, having an end-to-end understanding of the company’s working capital can help to inform strategic decisions more quickly and accurately, enable more effective communication with stakeholders, and pinpoint areas for improvement.

The need for a holistic approach to working capital management becomes even more pressing when viewed against a backdrop of supply chain disruption, rapid digitisation and business model re-engineering. Organisations are crying out for better cross-functional management of working capital along the entire chain, to enable them to be agile in response to shifting market conditions – and to answer important questions such as ‘How will working capital be impacted if our supply chain shortens?’

While the idea of having a working capital champion within the business to lead a cross-functional team is not new, the concept has evolved in recent years, driven largely by technology. Today, best practice in this area extends beyond horizontal working capital processes (as opposed to vertical functions) into instantly available cross-functional data.

Tapping into data

Historically, corporates have found it challenging to access data across departmental silos, certainly without manual workarounds, which inevitably lead to inaccuracies and delays. Game-changers here have been the advancement of enterprise resource planning (ERP) systems, like SAP S/4HANA, to include real-time databases and the addition of data visualisation tools such as Qlik and Tableau. Having all of these systems connected to each other in real time, via application programming interfaces (APIs), enables the democratisation of data – cross-functional dashboards can be set up to enable access for key individuals or entire teams. Furthermore, banks such as Bank of America are designing analytical tools that leverage the immense volume of data they already have on their clients to deliver greater insights including predictive programing. See box below.

Proving the value of Key Performance Indicators (KPIs) through the Client Insights Dashboard

For several years, Bank of America has offered clients a clear view into efficiency opportunities through its Client Insights Dashboard tool. Using bank data, the tool aggregates the data across various KPIs providing deeper views that help to pinpoint efficiency gaps across treasury processes such as in paper-to-electronic conversion, inefficient idle accounts, and accounts potentially exposed to fraud.It is all delivered through a customised dashboard that includes comparisons with industry peers for benchmarking.


With those insights comes an improved ability to manage the processes inherently involved in working capital, and to start seeking out those marginal gains. One of the areas where these potential efficiencies often hide is behind high-level metrics. Organisations frequently use key performance indicators, which are more like arbitrary numbers – with dipping for the line at the end of the year or quarter. Rarely do companies look beneath the headlines to achieve a deeper understanding of what’s driving those numbers.

The beauty of data visualisation tools is that they enable both a top-level view and a granular perspective. It’s possible to drill down to a transactional level and analyse how changes to workflows, supply chains or even business models will impact working capital. With the ‘cause and effect’ elements identified, technologies such as machine learning can be layered on top to deliver predictive analytics.

Corporates can use machine learning in an ‘unsupervised’ manner, which essentially enables an algorithm to detect unknown patterns in data; or they can use it in a ‘supervised’ way to collect data or produce a data output from previous experiences. The supervised side is particularly useful for forward planning around ‘known’ or ‘expected’ events, whereas the unsupervised style can help to identify unknowns that could impact the business.

 

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